Key result
A predictability framework based on Granger causality successfully determined atrial rhythm complexity and tracked propagation during atrial fibrillation without requiring activation detection.
The proposed Granger causality-based framework can analyze intracardiac signals to map AF propagation and complexity, potentially guiding catheter ablation interventions.
OBJECTIVE: This study introduces a predictability framework based on the concept of Granger causality (GC), in order to analyze the activity and interactions between different intracardiac sites during atrial fibrillation (AF). METHODS: GC-based interactions were studied using a three-electrode analysis scheme with multi-variate autoregressive models of the involved preprocessed intracardiac signals. The method was evaluated in different scenarios covering simulations of complex atrial activity as well as endocardial signals acquired from patients. RESULTS: The results illustrate the ability of the method to determine atrial rhythm complexity and to track and map propagation during AF. CONCLUSION: The proposed framework provides information on the underlying activation and regularity, does not require activation detection or postprocessing algorithms and is applicable for the analysis of any multielectrode catheter. SIGNIFICANCE: The proposed framework can potentially help to guide catheter ablation interventions of AF.
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Alcaine et al. (2016) studied Atrial fibrillation. Predictability framework based on Granger causality was evaluated on Ability to determine atrial rhythm complexity and track/map propagation. A predictability framework based on Granger causality successfully determined atrial rhythm complexity and tracked propagation during atrial fibrillation without requiring activation detection.
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